Te (Thomas) Tang

CEO and Co-founder of Anyware Robotics, based in the San Francisco Bay area. Lead a top-tier team to build the next-gen general purpose robots for heavy-duty tasks. Ex-FANUCer (world largest industrial robot manufacturer). Founding member of FANUC Silicon Valley Research Center. Lead AI vision product from scratch to commercialization. PhD from UC Berkeley, adviced by Prof. Masayoshi Tomizuka. Research focused on imitation learning and human-robot interaction. Honored with best paper awards and finalist two times. Authored a book available on Amazon.

Imitation Learning

Teach robots manipulation skills from human demonstration, then intelligently transfer to different scenarios. In this example, we are teaching robots to manipulate deformable objects (rope or clothes) which requires the adaptability to cope with varying shapes in each trail.

Motion adaptation to manipulate flexible objects

Motion Planning

Motion planning on high degree of freedom (DoF) is a challenging task. We need to control the robot arms to do dexterous tasks with real-time vision feedback, while at the same time, avoiding collision with environments or self-collision. Here we demonstrated the motion planning capability on a 18-DOF humanoid system (14-DoF arms, 2-Dof grippers, 1-DoF trunk, 1-DoF head).

Dual arm motion planning for wire harness assembly

Perception

Real-time tracking of featureless deformable objects (no markers). Tracking result was then used to plan the dual-arm robot motion.

Teach Robots Assembly from Human Demonstration

For robotic assembly tasks, tuning the parameters for force controller is a non-trival and time-consuming task. We proposed a novel learning-based framework to teach robots the optimal gains from human demonstration.

An Exoskeleton System for Hand Rehabilitation

Hand injuries seriously affect patients’ living qualities. To accelerate the recovery process, we designed a portable exoskeleton system to conduct hand rehabilitation exercise automatically.

To make the system extremely light, we innovated a compact motor system based on Ti-Ni shape memory alloy (shrink when heating).